Optimize ML inference for edge accelerators and GPUs using TensorRT, CUDA, Qualcomm QNN, Triton, MLIR, and ONNX. The team deploys large transformer-based models on low-cost, low-power edge devices for Wayve's first driving product. You'll work across compilers, runtimes, and kernels with Python and C++.

Build REST and GraphQL APIs and MCP servers in Python (FastAPI, Flask) to expose ML models from Databricks to internal apps, CRM platforms, and AI agents at RBC. Work within the Data Science team to architect the serving layer, handling contract design, authentication, and documentation.

This role involves building ML models for low-data drug discovery using active learning, meta-learning, fine-tuning, uncertainty estimation, and multimodal modeling, with tools like PyTorch and JAX. The scientist will design data acquisition strategies and closed-loop workflows for Lila’s AI-driven scientific discovery systems, collaborating with drug discovery and computational teams.

Work with Python, PyTorch, JAX, CUDA, Docker, Kubernetes, Slurm, Ray, and Linux on GPU computing. The Molecular Intelligence team builds ML and physics-based infrastructure for drug discovery, including biophysics, chemistry, cofolding models, simulation workflows, and agent-usable scientific pipelines.

Tesco★3.5 (25k)· London

You will work with Java, Spring Boot, Kafka, Apache Flink, Apache Beam, NoSQL databases, Redis, and Apache Camel. The Tesco Personalisation team builds real-time, low-latency services for recommendations, offers, and experimentation across web, mobile, and in-store channels using streaming data and ML.

Senior Research Engineer at Decagon will work with Python, modern ML tooling, and LLM post-training and deployment to build conversational AI models powering the company's enterprise support agents. You'll own multi-quarter initiatives improving model reliability, capability, and efficiency through research in instruction following, retrieval, memory, and task completion, taking projects from prototype to production. The role requires 5+ years in AI/ML engineering with proven production LLM deployment experience.

Lead a team of Data Scientists and ML Engineers building AI solutions for financial markets using LLMs, Generative AI, Deep Learning, Transformers, Agentic AI, Python, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel, Azure AI Foundry, AWS AI Services, and MLOps/LLMOps practices.

Design, build, and operate a Kubernetes-based MLOps platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. The platform supports the full model lifecycle—experimentation, deployment, and scalable inference—for AI, ML, and LLM workloads in production.

You'll build and operate a Kubernetes-based AI/ML platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. This role focuses on creating infrastructure, tooling, and guardrails so MLOps and data science teams can reliably deploy and serve models at scale.

Use deep learning frameworks, distributed computing, GPU-based training, and MLOps to build and deploy batch and real-time ML models. The Search & Recommendations team develops personalised recommendations, product ranking, and AI styling experiences for ASOS's online fashion platform, helping customers discover products.

C++ developers will work on ultra-low-latency exchange protocol encoders and automated trading strategies, using C++ and optionally Python, for a data-driven prop fund’s global automated trading systems. The role involves collaborating with researchers and ops in a small, fast-paced team operating in a real-time environment.

You will work with Python, LLMs, agentic workflows, retrieval-augmented generation (RAG), and internal AI platforms. The Tactical AI Automations team deploys and supports AI tools across PIMCO's Portfolio Management and Analytics groups, including operational support for Lane Assist and POET (portfolio optimization tools).

Lead 6-7 engineers on core ML infrastructure for a systematic quant fund: distributed model training, LLM hosting/fine-tuning, and scalable deployment. Use Python with working knowledge of C++/Java. Own technical direction for real-world, high-stakes systems.

The intern will design and build Python-based tools and mathematical models for real-world property decisions. Bowleys Oversight combines data, mathematical modeling, and AI to create residential property decision intelligence for landlords and investors. The role involves working directly with the founder to build the company’s analytical and technical foundations.

You'll work with Python, NLP/LLMs, SQL/NoSQL databases, and cloud infrastructure, designing retrieval, knowledge-base, and reasoning models. You own projects end-to-end at Catapult, which builds personalised AI that understands a user's full work context across email, calendar, Slack, and other tools. No team size is given for this founding role.

Waymo★3.0 (61)· London

You will use Python, deep learning frameworks (JAX, TensorFlow/PyTorch), and C++ (preferred) to develop data-selection algorithms and scaling laws for foundation model training. The AI Foundations team builds machine learning solutions for autonomous driving, focusing on reinforcement learning, generative modeling, and robust evaluation to improve the Waymo Driver. You will report to a Senior Staff Software Engineer.

This is a partnerships and business-development role, not a hands-on engineering position. You will work directly with founders, ML researchers, and scientists to create a global data-acquisition strategy for the company’s AI biology models. You will identify, negotiate, and secure complex data partnerships with health systems, biobanks, academic groups, and pharmaceutical companies.

EXL Service★3.7 (2k)· London

You will work with Python, FastAPI, LLM frameworks (prompt engineering, RAG, agentic systems), and cloud AI platforms (Azure OpenAI/AWS Bedrock/Claude) on Kubernetes. As a Sr. AI Architect & Client Partner at EXL, a data and AI services company, you will design and deliver production-grade Generative and Agentic AI systems for enterprise clients, converting ambiguous mandates into secure, scalable solutions while shaping deals and reusable IP.

Google★4.3 (6.2k)· London

Manage engineers building Google’s Learning Platforms within the Global Business Organization, which powers Google’s ecosystem of products and services for customers and partners. Work with back-end languages (Java, Python, Golang, C++) and front-end (JavaScript, TypeScript, HTML, CSS), plus ML model fine-tuning and evaluation.

Work with Python, PyTorch (or JAX/TensorFlow), RL fine-tuning frameworks like TRL or verl, and distributed multi-GPU training. The Reinforcement Learning Team advances RL, Bayesian optimisation, AI agents, LLMs, and vision-language models, applying them to AI for science, chemistry, physics, and robotics while publishing at top venues.

You'll work with Python, TensorFlow, PyTorch, TFX, Kubeflow Pipelines, and model serving technologies like TensorFlow Serving and Triton on machine learning systems for personalization, recommendation engines, and ranking algorithms. At Fractal, a strategic AI partner to Fortune 500 companies, you'll design and train ML models, build data pipelines for large-scale datasets, deploy models to production, and lead A/B testing and experimentation across a multidisciplinary team.

You will work with Python, LLM agents, inference/serving, reinforcement learning, multi-agent orchestration, search/retrieval pipelines, and modern AI coding tools. As a DeepMind Research Engineer, you bridge research and scalable systems, collaborating with scientists on machine forecasting to build 0-to-1 infrastructure, optimize LLM architectures and RL pipelines, and engineer evaluation platforms for autonomous reasoning under uncertainty.

This role develops generative AI applications using Java, Python, React or Angular, LangChain, LlamaIndex, Hugging Face, Docker, Kubernetes, and AWS, with tools like GitHub Copilot. You will build scalable, AI-powered solutions for Citi’s global finance operations.

You will use Python, KQL, SPL to build detection logic and automate response. You will protect Writer’s AI platform, training data, and model deployments, detecting threats like prompt injection and model extraction. You will coordinate incident response across teams and hunt for threats in GPU clusters and distributed training environments.

You will own a gradient-boosted ranking model (LambdaMART) benchmarked against a two-tower retrieval comparator, working in Python on AWS. The team is building a creator-to-product matching model and a multi-modal content model (video, speech, audio) trained on first-party affiliate conversion data to rank creators by actual sales, not follower counts. You will lead this R&D project for twelve months as the second of three technical hires.